Spatial Data: An IntroductionSpatial Data: An IntroductionKatherine CurtisKatherine CurtisGuest Lecture to SOC 674: Elementary Demographic Techniques Guest Lecture to SOC 674: Elementary Demographic Techniques November 25November 25thth, 2008, 2008WhatWhatWhy Why Who Who HowHow••What are What are ““spatial dataspatial data””??○○Data where, in addition to attribute values relating to Data where, in addition to attribute values relating to the primary phenomenon or phenomena of interest, the primary phenomenon or phenomena of interest, the the relative spatial locationsrelative spatial locationsof observations are also of observations are also recordedrecordedHousing prices for city blocksHousing prices for city blocksChild poverty rates for countiesChild poverty rates for countiesAccident counts by intersectionAccident counts by intersectionCancer incidence for Cancer incidence for geocodedgeocodedaddressesaddressesCountyCounty--toto--county migration streams for persons 65+county migration streams for persons 65+WhatWhatWhy Why Who Who HowHowGISGISSpatial Data Spatial Data AnalysisAnalysisSpatial Spatial AnalysisAnalysis““Spatial StatisticsSpatial Statistics””GeostatisticalGeostatisticalDataDataLatticeLatticeDataDataPointPoint--PatternPatternDataDataSpatial InteractionSpatial InteractionDataDataWhatWhatWhy Why Who Who HowHowGeostatisticalGeostatisticalDataDataU.S. TemperaturesU.S. TemperaturesWhatWhatWhy Why Who Who HowHowGeostatisticalGeostatisticalDataDataWisconsin ElevationWisconsin Elevation175 - 259260 - 343344 - 427428 - 511512 - 596Elevation (Meters)NApplied Population LaboratoryUW Extension Basin Educators In ServiceSource: USGS Digitial Elevation Model1 Degree DEM w ith 75m Cell SizeW isconsinElevation ModelWhatWhatWhy Why Who Who HowHowLattice DataLattice DataCivil Liberties for NationCivil Liberties for Nation--States from the World Freedom AtlasStates from the World Freedom AtlasWhatWhatWhy Why Who Who HowHowLattice DataLattice DataMexican Presidential Election, 2006Mexican Presidential Election, 2006WhatWhatWhy Why Who Who HowHowLattice DataLattice DataLog Odds of Proportion of Children in Poverty for U.S. Counties,Log Odds of Proportion of Children in Poverty for U.S. Counties,20002000WhatWhatWhy Why Who Who HowHowLattice DataLattice DataPercent Seasonal Housing for Wisconsin Watersheds, 2000Percent Seasonal Housing for Wisconsin Watersheds, 2000Percent Seasonal HUSSNL HU / TOT HU0.1% - 6.3%6.4% - 16%16.1% - 30.6%30.7% - 49.8%49.9% - 78.6%PointPoint--Pattern (Event) DataPattern (Event) DataA Week of Crimes in HollywoodA Week of Crimes in HollywoodWhatWhatWhy Why Who Who HowHowPointPoint--Pattern (Event) DataPattern (Event) DataLocation of Deaths from Cholera in Central London for Location of Deaths from Cholera in Central London for September 1854 (Snow)September 1854 (Snow)WhatWhatWhy Why Who Who HowHowWhatWhatWhy Why Who Who HowHowSpatial Flows DataSpatial Flows DataNorthwest Airlines Routes from AmsterdamNorthwest Airlines Routes from AmsterdamWhatWhatWhy Why Who Who HowHowSpatial Flows DataSpatial Flows DataMigration in the UK, Map 5 from Migration in the UK, Map 5 from RavensteinRavenstein18851885WhatWhatWhyWhyWho Who HowHow••Theoretical ImportanceTheoretical Importance○○A means of A means of organizing human activitiesorganizing human activitiesHuman Ecology, Contextual Models, etc.Human Ecology, Contextual Models, etc.••Statistical ImportanceStatistical Importance○○A means of A means of data integrationdata integrationVariable creationVariable creation○○A potentialA potentialproblemproblemSpatial autocorrelationSpatial autocorrelationWhatWhatWhyWhyWho Who HowHow••ToblerTobler’’ssFirst Law of GeographyFirst Law of Geography““Everything is related to everything else, but Everything is related to everything else, but near things are more related than distant thingsnear things are more related than distant things””Waldo ToblerEconomic Geography (1970:236)WhatWhatWhyWhyWho Who HowHow••Erroneous Statistical Inference and Substantive Erroneous Statistical Inference and Substantive ConclusionsConclusions““If a naIf a naïïve researcher completes a standard ve researcher completes a standard statistical analysis of statistical analysis of georeferencedgeoreferenceddata, it data, it does not follow that the data analytic results does not follow that the data analytic results have turned data into meaningful information have turned data into meaningful information merely because to the inexpert eye they are merely because to the inexpert eye they are indistinguishable from conventional statistical indistinguishable from conventional statistical results!results!””Daniel Griffith and Larry Layne(Oxford University Press, 1999:vii)WhatWhatWhy Why WhoWhoHowHow••PublicPublic••GeographersGeographers••EpidemiologistsEpidemiologists••CriminologistsCriminologists••Demographers!Demographers!○○Migration: spatial flowsMigration: spatial flows○○Fertility: diffusion of innovationFertility: diffusion of innovation○○Mortality: diffusion of diseaseMortality: diffusion of disease○○Urbanization: uneven developmentUrbanization: uneven developmentWhatWhatWhy Why WhoWhoHowHow••Goals of Spatial Data AnalysisGoals of Spatial Data Analysis○○Increase the Increase the basic understandingbasic understandingof some process of some process operating in spaceoperating in space○○Assess the evidence in favor of various Assess the evidence in favor of various hypotheseshypothesesconcerning that processconcerning that process○○Predict Predict valuesvaluesin areas where observations have not in areas where observations have not been madebeen made○○Examine the relationships among attributes Examine the relationships among attributes distributed in space, even when understanding of the distributed in space, even when understanding of the spatial process, spatial process, per seper se, is , is not a goalnot a goalWhatWhatWhy Why WhoWhoHowHow••Components of Spatial AnalysisComponents of Spatial Analysis○○VisualizationVisualizationShowingShowinginteresting patternsinteresting patterns○○Exploratory Spatial Data AnalysisExploratory Spatial Data AnalysisFindingFindinginteresting patternsinteresting patterns○○Spatial ModelingSpatial ModelingExplainingExplaininginteresting patternsinteresting patterns••Finding Interesting PatternsFinding